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Bug Assignee Prediction Using Association Rule Mining

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Computational Science and Its Applications -- ICCSA 2015 (ICCSA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9158))

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Abstract

In open source software development we have bug repository to which both developers and users can report bugs. Bug triage, deciding what to do with an incoming bug report, takes a large amount of developer resources and time. All newly coming bug reports must be triaged to determine whether the report is correct and requires attention and if it is, which potentially experienced developer/fixer will be assigned the responsibility of resolving the bug report. In this paper, we propose to apply association mining to assist in bug triage by using Apriori algorithm to predict the developer that should work on the bug based on the bug’s severity, priority and summary terms. We demonstrate our approach on collection of 1,695 bug reports of Thunderbird, AddOnSDK and Bugzilla products of Mozilla open source project. We have analyzed the association rules for top five assignee of the three products. Association rules can support the managers to improve its process during development and save time and resources.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Delhi, Delhi, India

    Meera Sharma

  2. Delhi College of Arts & Commerce, University of Delhi, Delhi, India

    Madhu Kumari & V. B. Singh

Authors
  1. Meera Sharma
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  2. Madhu Kumari
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  3. V. B. Singh
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Corresponding author

Correspondence to V. B. Singh .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Canaanland, Nigeria

    Sanjay Misra

  4. University of Calgary, Calgary, Alberta, Canada

    Marina L. Gavrilova

  5. University of Minho, Braga, Portugal

    Ana Maria Alves Coutinho Rocha

  6. Polytechnic University, Bari, Italy

    Carmelo Torre

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

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Sharma, M., Kumari, M., Singh, V.B. (2015). Bug Assignee Prediction Using Association Rule Mining. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2015. ICCSA 2015. Lecture Notes in Computer Science(), vol 9158. Springer, Cham. https://doi.org/10.1007/978-3-319-21410-8_35

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  • DOI: https://doi.org/10.1007/978-3-319-21410-8_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21409-2

  • Online ISBN: 978-3-319-21410-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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